Overview

Dataset statistics

Number of variables12
Number of observations457100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory152.2 MiB
Average record size in memory349.2 B

Variable types

Text2
DateTime2
Numeric8

Alerts

JamsCount is highly overall correlated with JamsDelay and 6 other fieldsHigh correlation
JamsDelay is highly overall correlated with JamsCount and 6 other fieldsHigh correlation
JamsLengthInKms is highly overall correlated with JamsCount and 5 other fieldsHigh correlation
MinsDelay is highly overall correlated with JamsCount and 6 other fieldsHigh correlation
TrafficIndexLive is highly overall correlated with JamsCount and 6 other fieldsHigh correlation
TrafficIndexWeekAgo is highly overall correlated with JamsCount and 6 other fieldsHigh correlation
TravelTimeHistoricPer10KmsMins is highly overall correlated with JamsCount and 5 other fieldsHigh correlation
TravelTimeLivePer10KmsMins is highly overall correlated with JamsCount and 6 other fieldsHigh correlation
TravelTimeLivePer10KmsMins has unique valuesUnique
TravelTimeHistoricPer10KmsMins has unique valuesUnique
MinsDelay has unique valuesUnique
JamsDelay has 107393 (23.5%) zerosZeros
TrafficIndexLive has 92030 (20.1%) zerosZeros
JamsLengthInKms has 109386 (23.9%) zerosZeros
JamsCount has 107393 (23.5%) zerosZeros
TrafficIndexWeekAgo has 89950 (19.7%) zerosZeros

Reproduction

Analysis started2024-11-17 13:17:08.076542
Analysis finished2024-11-17 13:17:16.435120
Duration8.36 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.2 MiB
2024-11-17T14:17:16.491507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1371300
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARE
2nd rowARE
3rd rowARE
4th rowARE
5th rowARE
ValueCountFrequency (%)
usa 93913
20.5%
deu 31920
 
7.0%
gbr 29819
 
6.5%
fra 29622
 
6.5%
ita 29495
 
6.5%
esp 29493
 
6.5%
nld 20047
 
4.4%
pol 14168
 
3.1%
can 14129
 
3.1%
tur 11916
 
2.6%
Other values (45) 152578
33.4%
2024-11-17T14:17:16.618549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 219111
16.0%
U 167258
12.2%
S 154064
11.2%
R 111540
 
8.1%
E 100402
 
7.3%
N 73236
 
5.3%
T 65166
 
4.8%
L 65046
 
4.7%
D 61417
 
4.5%
P 59182
 
4.3%
Other values (16) 294878
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1371300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 219111
16.0%
U 167258
12.2%
S 154064
11.2%
R 111540
 
8.1%
E 100402
 
7.3%
N 73236
 
5.3%
T 65166
 
4.8%
L 65046
 
4.7%
D 61417
 
4.5%
P 59182
 
4.3%
Other values (16) 294878
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1371300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 219111
16.0%
U 167258
12.2%
S 154064
11.2%
R 111540
 
8.1%
E 100402
 
7.3%
N 73236
 
5.3%
T 65166
 
4.8%
L 65046
 
4.7%
D 61417
 
4.5%
P 59182
 
4.3%
Other values (16) 294878
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1371300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 219111
16.0%
U 167258
12.2%
S 154064
11.2%
R 111540
 
8.1%
E 100402
 
7.3%
N 73236
 
5.3%
T 65166
 
4.8%
L 65046
 
4.7%
D 61417
 
4.5%
P 59182
 
4.3%
Other values (16) 294878
21.5%

City
Text

Distinct384
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.4 MiB
2024-11-17T14:17:16.747510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length28
Median length20
Mean length8.2014439
Min length4

Characters and Unicode

Total characters3748880
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowabu-dhabi
2nd rowabu-dhabi
3rd rowabu-dhabi
4th rowabu-dhabi
5th rowabu-dhabi
ValueCountFrequency (%)
birmingham 2377
 
0.5%
london 2367
 
0.5%
hamilton 2359
 
0.5%
manila 1227
 
0.3%
kuwait-city 1207
 
0.3%
kobe 1205
 
0.3%
reading 1204
 
0.3%
singapore 1203
 
0.3%
edinburgh 1203
 
0.3%
osaka 1203
 
0.3%
Other values (374) 441545
96.6%
2024-11-17T14:17:16.933588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 431125
 
11.5%
e 338791
 
9.0%
n 310657
 
8.3%
o 287924
 
7.7%
r 273957
 
7.3%
i 245717
 
6.6%
l 230116
 
6.1%
s 198082
 
5.3%
t 191174
 
5.1%
u 131137
 
3.5%
Other values (17) 1110200
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3748880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 431125
 
11.5%
e 338791
 
9.0%
n 310657
 
8.3%
o 287924
 
7.7%
r 273957
 
7.3%
i 245717
 
6.6%
l 230116
 
6.1%
s 198082
 
5.3%
t 191174
 
5.1%
u 131137
 
3.5%
Other values (17) 1110200
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3748880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 431125
 
11.5%
e 338791
 
9.0%
n 310657
 
8.3%
o 287924
 
7.7%
r 273957
 
7.3%
i 245717
 
6.6%
l 230116
 
6.1%
s 198082
 
5.3%
t 191174
 
5.1%
u 131137
 
3.5%
Other values (17) 1110200
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3748880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 431125
 
11.5%
e 338791
 
9.0%
n 310657
 
8.3%
o 287924
 
7.7%
r 273957
 
7.3%
i 245717
 
6.6%
l 230116
 
6.1%
s 198082
 
5.3%
t 191174
 
5.1%
u 131137
 
3.5%
Other values (17) 1110200
29.6%
Distinct30408
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Minimum2024-10-01 07:00:59.231000
Maximum2024-11-17 11:31:30.001000
2024-11-17T14:17:17.004677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:17.071230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

JamsDelay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24093
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.88355
Minimum0
Maximum15568.6
Zeros107393
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2024-11-17T14:17:17.135409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.1
median16.4
Q3101.9
95-th percentile879.3
Maximum15568.6
Range15568.6
Interquartile range (IQR)100.8

Descriptive statistics

Standard deviation585.42049
Coefficient of variation (CV)3.1836479
Kurtosis70.121023
Mean183.88355
Median Absolute Deviation (MAD)16.4
Skewness7.0911169
Sum84053172
Variance342717.15
MonotonicityNot monotonic
2024-11-17T14:17:17.196047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 107393
 
23.5%
2.6 2125
 
0.5%
2.8 1946
 
0.4%
2.7 1921
 
0.4%
2.5 1803
 
0.4%
3 1770
 
0.4%
2.9 1758
 
0.4%
3.1 1687
 
0.4%
1.1 1655
 
0.4%
3.3 1556
 
0.3%
Other values (24083) 333486
73.0%
ValueCountFrequency (%)
0 107393
23.5%
0.3 1
 
< 0.1%
0.4 330
 
0.1%
0.5 1078
 
0.2%
0.6 1149
 
0.3%
0.7 961
 
0.2%
0.8 905
 
0.2%
0.9 1054
 
0.2%
1 1394
 
0.3%
1.1 1655
 
0.4%
ValueCountFrequency (%)
15568.6 1
< 0.1%
15503.2 1
< 0.1%
15079.8 1
< 0.1%
13946.4 1
< 0.1%
13125.4 1
< 0.1%
13090.9 1
< 0.1%
12717 1
< 0.1%
12582.6 1
< 0.1%
12470.9 1
< 0.1%
12465.9 1
< 0.1%

TrafficIndexLive
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct176
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.508068
Minimum0
Maximum221
Zeros92030
Zeros (%)20.1%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2024-11-17T14:17:17.257577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median14
Q330
95-th percentile61
Maximum221
Range221
Interquartile range (IQR)28

Descriptive statistics

Standard deviation20.376544
Coefficient of variation (CV)1.0445188
Kurtosis2.2908557
Mean19.508068
Median Absolute Deviation (MAD)13
Skewness1.3880882
Sum8917138
Variance415.20355
MonotonicityNot monotonic
2024-11-17T14:17:17.316163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 92030
 
20.1%
1 13498
 
3.0%
2 12376
 
2.7%
3 11241
 
2.5%
4 10522
 
2.3%
5 10130
 
2.2%
6 9876
 
2.2%
7 9622
 
2.1%
8 9538
 
2.1%
9 9487
 
2.1%
Other values (166) 268780
58.8%
ValueCountFrequency (%)
0 92030
20.1%
1 13498
 
3.0%
2 12376
 
2.7%
3 11241
 
2.5%
4 10522
 
2.3%
5 10130
 
2.2%
6 9876
 
2.2%
7 9622
 
2.1%
8 9538
 
2.1%
9 9487
 
2.1%
ValueCountFrequency (%)
221 1
 
< 0.1%
219 1
 
< 0.1%
210 1
 
< 0.1%
194 1
 
< 0.1%
193 1
 
< 0.1%
192 1
 
< 0.1%
181 2
< 0.1%
175 3
< 0.1%
173 1
 
< 0.1%
172 1
 
< 0.1%

JamsLengthInKms
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8252
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.182389
Minimum0
Maximum1918
Zeros109386
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2024-11-17T14:17:17.373949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median2.5
Q318.4
95-th percentile170.4
Maximum1918
Range1918
Interquartile range (IQR)18.3

Descriptive statistics

Standard deviation100.32101
Coefficient of variation (CV)3.023321
Kurtosis50.532547
Mean33.182389
Median Absolute Deviation (MAD)2.5
Skewness6.1959777
Sum15167670
Variance10064.306
MonotonicityNot monotonic
2024-11-17T14:17:17.438903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 109386
 
23.9%
0.2 14534
 
3.2%
0.1 10636
 
2.3%
0.3 10227
 
2.2%
0.4 8200
 
1.8%
0.5 6914
 
1.5%
0.6 5964
 
1.3%
0.7 5386
 
1.2%
0.8 4901
 
1.1%
0.9 4560
 
1.0%
Other values (8242) 276392
60.5%
ValueCountFrequency (%)
0 109386
23.9%
0.1 10636
 
2.3%
0.2 14534
 
3.2%
0.3 10227
 
2.2%
0.4 8200
 
1.8%
0.5 6914
 
1.5%
0.6 5964
 
1.3%
0.7 5386
 
1.2%
0.8 4901
 
1.1%
0.9 4560
 
1.0%
ValueCountFrequency (%)
1918 1
< 0.1%
1758.1 1
< 0.1%
1757.3 1
< 0.1%
1727.6 1
< 0.1%
1690.1 1
< 0.1%
1686.7 1
< 0.1%
1666.1 1
< 0.1%
1660.4 1
< 0.1%
1653.2 1
< 0.1%
1640 1
< 0.1%

JamsCount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1760
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.666392
Minimum0
Maximum3102
Zeros107393
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2024-11-17T14:17:17.540190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q329
95-th percentile219
Maximum3102
Range3102
Interquartile range (IQR)28

Descriptive statistics

Standard deviation133.81254
Coefficient of variation (CV)2.9302192
Kurtosis55.471919
Mean45.666392
Median Absolute Deviation (MAD)5
Skewness6.405768
Sum20874108
Variance17905.795
MonotonicityNot monotonic
2024-11-17T14:17:17.597898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 107393
23.5%
1 47403
 
10.4%
2 28279
 
6.2%
3 19968
 
4.4%
4 15533
 
3.4%
5 12907
 
2.8%
6 11020
 
2.4%
7 9581
 
2.1%
8 8413
 
1.8%
9 7461
 
1.6%
Other values (1750) 189142
41.4%
ValueCountFrequency (%)
0 107393
23.5%
1 47403
10.4%
2 28279
 
6.2%
3 19968
 
4.4%
4 15533
 
3.4%
5 12907
 
2.8%
6 11020
 
2.4%
7 9581
 
2.1%
8 8413
 
1.8%
9 7461
 
1.6%
ValueCountFrequency (%)
3102 1
< 0.1%
3007 1
< 0.1%
2962 1
< 0.1%
2796 1
< 0.1%
2656 1
< 0.1%
2637 1
< 0.1%
2626 1
< 0.1%
2487 1
< 0.1%
2466 1
< 0.1%
2378 1
< 0.1%

TrafficIndexWeekAgo
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct172
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.783448
Minimum0
Maximum221
Zeros89950
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2024-11-17T14:17:17.656759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median14
Q330
95-th percentile61
Maximum221
Range221
Interquartile range (IQR)28

Descriptive statistics

Standard deviation20.425471
Coefficient of variation (CV)1.0324525
Kurtosis2.1378074
Mean19.783448
Median Absolute Deviation (MAD)13
Skewness1.3555772
Sum9043014
Variance417.19986
MonotonicityNot monotonic
2024-11-17T14:17:17.716207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 89950
 
19.7%
1 13243
 
2.9%
2 12232
 
2.7%
3 11119
 
2.4%
4 10506
 
2.3%
5 10005
 
2.2%
6 9764
 
2.1%
7 9480
 
2.1%
9 9451
 
2.1%
8 9358
 
2.0%
Other values (162) 271992
59.5%
ValueCountFrequency (%)
0 89950
19.7%
1 13243
 
2.9%
2 12232
 
2.7%
3 11119
 
2.4%
4 10506
 
2.3%
5 10005
 
2.2%
6 9764
 
2.1%
7 9480
 
2.1%
8 9358
 
2.0%
9 9451
 
2.1%
ValueCountFrequency (%)
221 1
< 0.1%
219 1
< 0.1%
210 1
< 0.1%
193 1
< 0.1%
192 1
< 0.1%
181 2
< 0.1%
175 2
< 0.1%
173 1
< 0.1%
168 1
< 0.1%
167 1
< 0.1%
Distinct30009
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
Minimum2024-09-24 06:31:00
Maximum2024-11-10 12:16:30
2024-11-17T14:17:17.774828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:17.841189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TravelTimeLivePer10KmsMins
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct457100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.812645
Minimum5.7764833
Maximum46.887477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2024-11-17T14:17:17.904627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.7764833
5-th percentile8.4160899
Q110.95649
median13.111839
Q315.820933
95-th percentile21.548827
Maximum46.887477
Range41.110994
Interquartile range (IQR)4.8644432

Descriptive statistics

Standard deviation4.1637788
Coefficient of variation (CV)0.30144689
Kurtosis3.4290628
Mean13.812645
Median Absolute Deviation (MAD)2.3799481
Skewness1.3587791
Sum6313760
Variance17.337054
MonotonicityNot monotonic
2024-11-17T14:17:17.959504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.44007779 1
 
< 0.1%
14.38688337 1
 
< 0.1%
9.150249501 1
 
< 0.1%
8.411789678 1
 
< 0.1%
8.214035932 1
 
< 0.1%
7.917927296 1
 
< 0.1%
7.90137162 1
 
< 0.1%
7.956428884 1
 
< 0.1%
7.965113859 1
 
< 0.1%
9.233829145 1
 
< 0.1%
Other values (457090) 457090
> 99.9%
ValueCountFrequency (%)
5.776483271 1
< 0.1%
5.831333938 1
< 0.1%
5.831833009 1
< 0.1%
5.834200285 1
< 0.1%
5.839213766 1
< 0.1%
5.854329187 1
< 0.1%
5.85580656 1
< 0.1%
5.860116819 1
< 0.1%
5.868758867 1
< 0.1%
5.869186603 1
< 0.1%
ValueCountFrequency (%)
46.88747696 1
< 0.1%
46.26949178 1
< 0.1%
45.93734285 1
< 0.1%
45.92560347 1
< 0.1%
45.34972789 1
< 0.1%
45.3164593 1
< 0.1%
45.17186128 1
< 0.1%
45.04707754 1
< 0.1%
44.96962674 1
< 0.1%
44.85698921 1
< 0.1%

TravelTimeHistoricPer10KmsMins
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct457100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.118687
Minimum5.8686899
Maximum36.7602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2024-11-17T14:17:18.012655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.8686899
5-th percentile8.4097922
Q110.690081
median12.591903
Q314.886538
95-th percentile19.522039
Maximum36.7602
Range30.89151
Interquartile range (IQR)4.196457

Descriptive statistics

Standard deviation3.5526398
Coefficient of variation (CV)0.27080757
Kurtosis3.2826281
Mean13.118687
Median Absolute Deviation (MAD)2.065687
Skewness1.3122071
Sum5996551.9
Variance12.62125
MonotonicityNot monotonic
2024-11-17T14:17:18.071549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.550711374 1
 
< 0.1%
12.06822416 1
 
< 0.1%
9.215192301 1
 
< 0.1%
8.547720595 1
 
< 0.1%
8.311061937 1
 
< 0.1%
7.846066317 1
 
< 0.1%
7.686530387 1
 
< 0.1%
7.785518161 1
 
< 0.1%
7.737355344 1
 
< 0.1%
8.96071192 1
 
< 0.1%
Other values (457090) 457090
> 99.9%
ValueCountFrequency (%)
5.868689949 1
< 0.1%
5.87744231 1
< 0.1%
5.888246626 1
< 0.1%
5.904652205 1
< 0.1%
5.918415195 1
< 0.1%
5.932137416 1
< 0.1%
5.945464936 1
< 0.1%
5.955924955 1
< 0.1%
5.966648468 1
< 0.1%
5.981403825 1
< 0.1%
ValueCountFrequency (%)
36.76019957 1
< 0.1%
36.46664313 1
< 0.1%
36.25273362 1
< 0.1%
36.17668883 1
< 0.1%
35.7606891 1
< 0.1%
35.64287755 1
< 0.1%
35.62543869 1
< 0.1%
35.53547586 1
< 0.1%
35.52896702 1
< 0.1%
35.50102837 1
< 0.1%

MinsDelay
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct457100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69395763
Minimum-11.695612
Maximum20.499677
Zeros0
Zeros (%)0.0%
Negative87657
Negative (%)19.2%
Memory size3.5 MiB
2024-11-17T14:17:18.131127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-11.695612
5-th percentile-0.24230507
Q10.066533428
median0.37274832
Q30.96306094
95-th percentile2.8161412
Maximum20.499677
Range32.195289
Interquartile range (IQR)0.89652751

Descriptive statistics

Standard deviation1.0978297
Coefficient of variation (CV)1.5819838
Kurtosis13.59648
Mean0.69395763
Median Absolute Deviation (MAD)0.37900224
Skewness2.4874348
Sum317208.03
Variance1.2052301
MonotonicityNot monotonic
2024-11-17T14:17:18.189641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1106335845 1
 
< 0.1%
2.31865921 1
 
< 0.1%
-0.06494280014 1
 
< 0.1%
-0.1359309165 1
 
< 0.1%
-0.09702600539 1
 
< 0.1%
0.07186097887 1
 
< 0.1%
0.2148412331 1
 
< 0.1%
0.1709107236 1
 
< 0.1%
0.2277585148 1
 
< 0.1%
0.2731172255 1
 
< 0.1%
Other values (457090) 457090
> 99.9%
ValueCountFrequency (%)
-11.69561206 1
< 0.1%
-10.8555978 1
< 0.1%
-10.70780758 1
< 0.1%
-9.939216213 1
< 0.1%
-9.712700717 1
< 0.1%
-9.682370834 1
< 0.1%
-9.432611487 1
< 0.1%
-9.138384452 1
< 0.1%
-9.110297858 1
< 0.1%
-8.959010291 1
< 0.1%
ValueCountFrequency (%)
20.49967721 1
< 0.1%
20.37719588 1
< 0.1%
20.34223625 1
< 0.1%
20.1674452 1
< 0.1%
20.0642045 1
< 0.1%
19.30786826 1
< 0.1%
18.76104088 1
< 0.1%
17.15354808 1
< 0.1%
17.12523348 1
< 0.1%
16.07952071 1
< 0.1%

Interactions

2024-11-17T14:17:15.332486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.083700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.639917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.063170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.506002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.940177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.415602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.877158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:15.390203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.167660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.696227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.122038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.562740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.997662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.495960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.938671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:15.443482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.246809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.745840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.173362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.615209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.048430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.552432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.993493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:15.502270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.332960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.802712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.232420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.672256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.106370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.613196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:15.054215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:15.556409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.390341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.855674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.288300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.725264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.199515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.668320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:15.111825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:15.609300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.443495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.906295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.341076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.776207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.249050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.718722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:15.165833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:15.659712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.495475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.955359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.392931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.826562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.300561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.767933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:15.218982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:15.715642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:12.552400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.010752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.449767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:13.882869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.359042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:14.823267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-17T14:17:15.276182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-11-17T14:17:18.230641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
JamsCountJamsDelayJamsLengthInKmsMinsDelayTrafficIndexLiveTrafficIndexWeekAgoTravelTimeHistoricPer10KmsMinsTravelTimeLivePer10KmsMins
JamsCount1.0000.9940.9800.6120.8000.7550.5180.591
JamsDelay0.9941.0000.9780.6230.8050.7560.5180.594
JamsLengthInKms0.9800.9781.0000.6200.7930.7440.4830.563
MinsDelay0.6120.6230.6201.0000.7300.6260.5000.635
TrafficIndexLive0.8000.8050.7930.7301.0000.9380.6310.714
TrafficIndexWeekAgo0.7550.7560.7440.6260.9381.0000.6040.666
TravelTimeHistoricPer10KmsMins0.5180.5180.4830.5000.6310.6041.0000.980
TravelTimeLivePer10KmsMins0.5910.5940.5630.6350.7140.6660.9801.000

Missing values

2024-11-17T14:17:15.842043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-17T14:17:16.080732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountryCityUpdateTimeUTCJamsDelayTrafficIndexLiveJamsLengthInKmsJamsCountTrafficIndexWeekAgoUpdateTimeUTCWeekAgoTravelTimeLivePer10KmsMinsTravelTimeHistoricPer10KmsMinsMinsDelay
0AREabu-dhabi2024-11-17 11:01:00.00027.58.04.69.08.02024-11-10 11:01:00.0009.4400789.550711-0.110634
1AREabu-dhabi2024-11-17 10:01:00.00067.29.08.321.09.02024-11-10 10:01:00.0019.6702449.704906-0.034662
2AREabu-dhabi2024-11-17 09:01:00.00031.89.03.412.09.02024-11-10 09:01:00.0019.6372259.736813-0.099587
3AREabu-dhabi2024-11-17 08:01:30.00189.310.010.230.010.02024-11-10 08:01:30.0009.7271779.738622-0.011445
4AREabu-dhabi2024-11-17 07:46:30.00170.710.010.722.010.02024-11-10 07:46:30.0009.7180039.750348-0.032346
5AREabu-dhabi2024-11-17 07:01:30.00036.79.05.713.010.02024-11-10 07:46:30.0009.5545649.630718-0.076154
6AREabu-dhabi2024-11-17 06:31:30.00031.08.02.47.08.02024-11-10 06:31:30.0009.4683289.556803-0.088475
7AREabu-dhabi2024-11-17 05:01:00.00020.38.06.36.08.02024-11-10 05:01:00.0009.3157239.2715900.044133
8AREabu-dhabi2024-11-17 04:01:30.00149.08.010.914.09.02024-11-10 04:01:30.0019.0727529.0030360.069715
9AREabu-dhabi2024-11-17 03:16:30.00039.58.012.011.010.02024-11-10 03:31:30.0009.0385389.0022240.036314
CountryCityUpdateTimeUTCJamsDelayTrafficIndexLiveJamsLengthInKmsJamsCountTrafficIndexWeekAgoUpdateTimeUTCWeekAgoTravelTimeLivePer10KmsMinsTravelTimeHistoricPer10KmsMinsMinsDelay
457090ZAFpretoria2024-10-01 17:01:30.00087.623.08.626.013.02024-09-24 17:01:30.00014.19544513.4037060.791740
457091ZAFpretoria2024-10-01 16:01:30.000194.140.036.654.014.02024-09-24 16:31:30.00116.00215414.5384731.463681
457092ZAFpretoria2024-10-01 15:01:30.000528.051.0118.9148.013.02024-09-24 15:01:30.00017.37662615.7158951.660731
457093ZAFpretoria2024-10-01 14:46:30.001588.449.0115.5150.013.02024-09-24 14:01:30.00017.18188115.7002031.481678
457094ZAFpretoria2024-10-01 13:46:30.000168.230.029.357.013.02024-09-24 13:01:30.00015.00195914.3761270.625833
457095ZAFpretoria2024-10-01 12:31:30.000195.127.027.266.014.02024-09-24 12:01:30.00114.93088014.1292150.801665
457096ZAFpretoria2024-10-01 11:31:30.00078.322.09.227.015.02024-09-24 11:01:30.00013.97328513.6308190.342466
457097ZAFpretoria2024-10-01 10:01:30.00085.021.09.729.016.02024-09-24 10:16:30.00113.69049113.2873020.403189
457098ZAFpretoria2024-10-01 09:01:30.00068.120.06.723.015.02024-09-24 09:46:30.00013.38720312.9835580.403646
457099ZAFpretoria2024-10-01 08:46:30.00067.720.06.622.013.02024-09-24 08:46:30.00013.31149212.8986240.412867